Problem Solved
The invention addresses the challenge of detecting rare earth metals and elements (REMEs) in water, which is crucial for environmental monitoring and resource management. Traditional methods often lack efficiency, accuracy, or the ability to process real-time data, making it difficult to detect these elements promptly and accurately.
Core Features
The system includes a computer processor, sensors, a data analysis module, and a machine learning module. The sensors detect various attributes of the fluid, such as pH and conductivity. The machine learning module predicts the presence of REMEs by analyzing sensor data, offering both supervised and unsupervised learning techniques.
Inventive Step
This invention uniquely combines advanced sensors with machine learning to detect REMEs in real-time. Unlike existing solutions, it uses a suite of sensors optimized for specific water quality parameters and employs machine learning to identify unique REME signatures, distinguishing between natural and elevated levels.
Benefits
The system provides accurate, real-time detection of REMEs, enhancing environmental monitoring and resource management. It can generate alerts and reports for different user types, such as environmental agencies or mining companies, facilitating informed decision-making.
Broader Impact
This invention can significantly improve environmental sustainability by enabling better monitoring of water quality and resource management. It supports industries like mining and environmental protection, promoting more responsible and efficient use of natural resources.